Mohamed Sadek Saadi
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Has OpenGVLab Lumina Outperformed OpenAI’s Model?
We’ve just released the results from a large-scale human evaluation (400k annotations) of OpenGVLab’s newest text-to-image model, Lumina. Surprisingly, Lumina outperforms OpenAI’s DALL-E 3 in terms of alignment, although it ranks #6 in our overall human preference benchmark.
To support further development in text-to-image models, we’re making our entire human-annotated dataset publicly available. If you’re working on model improvements and need high-quality data, feel free to explore.
We welcome your feedback and look forward to any insights you might share!
https://huggingface.co./datasets/Rapidata/OpenGVLab_Lumina_t2i_human_preference
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Rapidata/OpenGVLab_Lumina_t2i_human_preference
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This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!
All that at less than the cost of a single hour of a typical ML engineer in Zurich!
The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style, Speed of Time flow and Quality of Physics.
https://huggingface.co./datasets/Rapidata/text-2-video-Rich-Human-Feedback
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